code stringlengths 81 54k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
if is_sentencepiece_available():
from ..ta.... | 13 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a = {"configuration_sew": ["SEW_PRETRAINED_CONFIG_ARCHIVE_MAP", "SEWConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvail... | 13 | 1 |
'''simple docstring'''
import darl # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline
a = {
"n_samples": 64,
"horizon": 32,
"num_inference_steps": 20,
"n_guide_steps": 2, # can set to 0 for faster sampling, does not use value network
"scale_... | 13 |
'''simple docstring'''
import requests
from bsa import BeautifulSoup
def __magic_name__ ( __UpperCAmelCase = "AAPL" ) -> str:
'''simple docstring'''
__SCREAMING_SNAKE_CASE = f"""https://in.finance.yahoo.com/quote/{symbol}?s={symbol}"""
__SCREAMI... | 13 | 1 |
'''simple docstring'''
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a = logging.get_logger(__name__)
a = {
"huggingface/time-series-transformer-tourism-monthly": (
"https://huggingface.co/huggingf... | 13 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase ) -> bool:
'''simple docstring'''
if num < 0:
return False
__SCREAMING_SNAKE_CASE = num
__SCREAMING_SNAKE_CASE = 0
while num > 0:
__SCREAMING_SNAKE_... | 13 | 1 |
'''simple docstring'''
import math
def __magic_name__ ( __UpperCAmelCase ) -> bool:
'''simple docstring'''
return math.sqrt(__UpperCAmelCase ) * math.sqrt(__UpperCAmelCase ) == num
def __magic_name__ ( __UpperCAmelCase ) -> ... | 13 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
a = list[list[float | int]]
def __magic_name__ ( __UpperCAmelCase , __UpperCAmelCase ) -> Matrix:
'''simple docstring'''
__SCREAMING_SNAKE_CASE ... | 13 | 1 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer
a = logging.get_logger(__name__)
a ... | 13 |
'''simple docstring'''
from collections import UserDict
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available... | 13 | 1 |
'''simple docstring'''
from typing import List, Optional
import numpy as np
from ...processing_utils import ProcessorMixin
from ...utils import to_numpy
class __a ( _snake_case ):
__UpperCamelCase : Optional[Any] = 'EncodecFeatureExtractor'
__UpperCamelCase : Dict = ('T5Tokeniz... | 13 |
'''simple docstring'''
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be check... | 13 | 1 |
'''simple docstring'''
from random import shuffle
import tensorflow as tf
from numpy import array
def __magic_name__ ( __UpperCAmelCase , __UpperCAmelCase ) -> Optional[Any]:
'''simple docstring'''
__SCREAMING_SNAKE_CASE = int(__UpperCAmelCa... | 13 |
'''simple docstring'''
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
BartForSequen... | 13 | 1 |
'''simple docstring'''
import collections
import importlib.util
import os
import re
from pathlib import Path
a = "src/transformers"
# Matches is_xxx_available()
a = re.compile(r"is\_([a-z_]*)_available()")
# Catches a one-line _import_struct = {xxx}
a = re.compile(r"^_impo... | 13 |
'''simple docstring'''
from ...utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_torch_available,
is_transformers_available,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAv... | 13 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor
a = logging.get_logger(__name__)
class __a ( _snake_case ):
def __init__( self : Optional[Any] ,*lowerCamelCase : List[str] ,... | 13 |
'''simple docstring'''
import requests
from bsa import BeautifulSoup
def __magic_name__ ( __UpperCAmelCase , __UpperCAmelCase ) -> str:
'''simple docstring'''
__SCREAMING_SNAKE_CASE = BeautifulSoup(requests.get(__UpperCAmelCase , params... | 13 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a = {
"configuration_clipseg": [
"CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP",
"CLIPSegConfig",
"CLIPSegTextConfig",
"CLIPSegV... | 13 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a = logging.get_logger(__name__)
a = {
"camembert-base": "https://huggingface.co/ca... | 13 | 1 |
'''simple docstring'''
import logging
from transformers.configuration_utils import PretrainedConfig
a = logging.getLogger(__name__)
class __a ( _snake_case ):
__UpperCamelCase : List[Any] = 'masked_bert'
def __init__( self : Optional[int] ,lowerCamelCase ... | 13 |
'''simple docstring'''
import inspect
import unittest
import numpy as np
from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_t... | 13 | 1 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase ) -> str:
'''simple docstring'''
if isinstance(__UpperCAmelCase , __UpperCAmelCase ):
raise TypeError("""'float' object cannot be interpreted as an integer""" )
if isinstance(__Uppe... | 13 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a = logging.get_logger(__name__)
a = {
"asapp/sew-tiny-100k": "https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json",
# See a... | 13 | 1 |
'''simple docstring'''
import numpy as np
import datasets
a = "\nCompute the Mahalanobis Distance\n\nMahalonobis distance is the distance between a point and a distribution.\nAnd not between two distinct points. It is effectively a multivariate equivalent of the Euclidean distance.\nIt was introdu... | 13 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase = 1 , __UpperCAmelCase = 1000 ) -> int:
'''simple docstring'''
__SCREAMING_SNAKE_CASE = 1
__SCREAMING_SNAKE_CASE = 0
for divide_by_number in range(__UpperCAm... | 13 | 1 |
'''simple docstring'''
from __future__ import annotations
def __magic_name__ ( __UpperCAmelCase ) -> bool:
'''simple docstring'''
__SCREAMING_SNAKE_CASE = str(__UpperCAmelCase )
return len(__UpperCAmelCase ) == 9 and set(__UpperCAmelCa... | 13 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin
if is_torch_available():
import... | 13 | 1 |
'''simple docstring'''
from sklearn.metrics import matthews_corrcoef
import datasets
a = "\nCompute the Matthews correlation coefficient (MCC)\n\nThe Matthews correlation coefficient is used in machine learning as a\nmeasure of the quality of binary and multiclass classifications. It takes\ninto a... | 13 |
'''simple docstring'''
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def __magic_name__ ( __UpperCAmelCase ) -> Tuple:
'''simple docstring'''
def wrapper(*__UpperCAmel... | 13 | 1 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer
a = logging.get_logger(__name__)
a ... | 13 |
'''simple docstring'''
import time
from contextlib import contextmanager
from pathlib import Path
import pytest
import requests
from huggingface_hub.hf_api import HfApi, HfFolder
a = "__DUMMY_TRANSFORMERS_USER__"
a = "Dummy User"
a = "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"
... | 13 | 1 |
'''simple docstring'''
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
a = (720, 1280) # Height, Width
a = (0.4, 0.6) # if height or width lower than this scale, drop it.
a = 1 / 100
a = ... | 13 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
a = logging.get_logger(__name__)
a = {
"ut/deta": "https://huggingface.co/ut/deta/resolve/main/config.json",
}
class __a ( ... | 13 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a = logging.get_logger(__name__)
a = {
"sail/poolfor... | 13 |
'''simple docstring'''
import flax.linen as nn
import jax
import jax.numpy as jnp
class __a ( nn.Module ):
__UpperCamelCase : int
__UpperCamelCase : jnp.dtype = jnp.floataa
def UpperCAmelCase__ ( self : List[Any] ):
'''simple docstring'''
__SC... | 13 | 1 |
'''simple docstring'''
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, PLBartTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
from ...t... | 13 |
'''simple docstring'''
import sys
from collections import defaultdict
class __a :
def __init__( self : Dict ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = []
def UpperCAmelCase__ ( self : List[Any] ,lowerCamelCase : ... | 13 | 1 |
'''simple docstring'''
import argparse
import logging
import os
from datetime import datetime
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm
from transformers import GPTaLMHeadModel
a = logging.getLo... | 13 |
'''simple docstring'''
import os
import string
import sys
a = 1 << 8
a = {
"tab": ord("\t"),
"newline": ord("\r"),
"esc": 27,
"up": 65 + ARROW_KEY_FLAG,
"down": 66 + ARROW_KEY_FLAG,
"right": 67 + ARROW_KEY_FLAG,
"left": 68 + ARROW_KEY_FLAG,
"mod_int": 91... | 13 | 1 |
'''simple docstring'''
a = [
[0, 16, 13, 0, 0, 0],
[0, 0, 10, 12, 0, 0],
[0, 4, 0, 0, 14, 0],
[0, 0, 9, 0, 0, 20],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
def __magic_name__ ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __Upp... | 13 |
'''simple docstring'''
from __future__ import annotations
import bisect
def __magic_name__ ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = 0 , __UpperCAmelCase = -1 ) -> int:
'''simple docstring'''
if hi < 0:
__SCREAMING_SNAKE_CAS... | 13 | 1 |
'''simple docstring'''
from __future__ import annotations
def __magic_name__ ( __UpperCAmelCase , __UpperCAmelCase ) -> list[str]:
'''simple docstring'''
if nth_term == "":
return [""]
__SCREAMING_SNAKE_CASE = int(__UpperCAmelCase ... | 13 |
'''simple docstring'''
import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
a = logging.get_logger(__name__)
class __a ( _snake_case ):
__UpperCamelCase : int... | 13 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a = logging.get_logger(__name__)
a = {
"naver-clova-ix/donut-base": "https://huggingface.co/naver-clova-ix/donut-base/resolve/main/config.json",
# See all Donut models at htt... | 13 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a = {"configuration_sew": ["SEW_PRETRAINED_CONFIG_ARCHIVE_MAP", "SEWConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvail... | 13 | 1 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase = 1000 ) -> int:
'''simple docstring'''
__SCREAMING_SNAKE_CASE = 3
__SCREAMING_SNAKE_CASE = 0
while a < n:
if a % 3 == 0 or a % 5 == 0:
result += a
... | 13 |
'''simple docstring'''
import requests
from bsa import BeautifulSoup
def __magic_name__ ( __UpperCAmelCase = "AAPL" ) -> str:
'''simple docstring'''
__SCREAMING_SNAKE_CASE = f"""https://in.finance.yahoo.com/quote/{symbol}?s={symbol}"""
__SCREAMI... | 13 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Iterator
class __a :
def __init__( self : Optional[int] ,lowerCamelCase : int ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = value
__SCREAMING_SNAKE_... | 13 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase ) -> bool:
'''simple docstring'''
if num < 0:
return False
__SCREAMING_SNAKE_CASE = num
__SCREAMING_SNAKE_CASE = 0
while num > 0:
__SCREAMING_SNAKE_... | 13 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a = logging.get_logger(__name__)
a = {
"xlm-roberta-base": "https://huggingface.co/... | 13 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
a = list[list[float | int]]
def __magic_name__ ( __UpperCAmelCase , __UpperCAmelCase ) -> Matrix:
'''simple docstring'''
__SCREAMING_SNAKE_CASE ... | 13 | 1 |
'''simple docstring'''
from __future__ import annotations
def __magic_name__ ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , ) -> None:
'''simple docstring'''
__SCREAMING_SNAKE_CASE ... | 13 |
'''simple docstring'''
from collections import UserDict
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available... | 13 | 1 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vis... | 13 |
'''simple docstring'''
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be check... | 13 | 1 |
'''simple docstring'''
import argparse
import copy
def __magic_name__ ( __UpperCAmelCase ) -> Optional[int]:
'''simple docstring'''
__SCREAMING_SNAKE_CASE = {}
with open(__UpperCAmelCase ) as f:
for line in f:
if line.split(... | 13 |
'''simple docstring'''
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
BartForSequen... | 13 | 1 |
'''simple docstring'''
import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union
if TYPE_CHECKING:
from ...processing_utils import ProcessorMixin
from ...utils import TensorType
from ...configuration_utils import PretrainedConfig
fr... | 13 |
'''simple docstring'''
from ...utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_torch_available,
is_transformers_available,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAv... | 13 | 1 |
'''simple docstring'''
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging
a = logging.get_l... | 13 |
'''simple docstring'''
import requests
from bsa import BeautifulSoup
def __magic_name__ ( __UpperCAmelCase , __UpperCAmelCase ) -> str:
'''simple docstring'''
__SCREAMING_SNAKE_CASE = BeautifulSoup(requests.get(__UpperCAmelCase , params... | 13 | 1 |
'''simple docstring'''
from arguments import InitializationArguments
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser
# Configuration
a = HfArgumentParser(InitializationArguments)
a = parser.parse_args()
# Load codeparrot tokenizer trained for... | 13 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a = logging.get_logger(__name__)
a = {
"camembert-base": "https://huggingface.co/ca... | 13 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a = {
"configuration_blip_2": [
"BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP",
"Blip2Config",
"Blip2QFormerConfig",
"Blip2Vision... | 13 |
'''simple docstring'''
import inspect
import unittest
import numpy as np
from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_t... | 13 | 1 |
'''simple docstring'''
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def __magic_name__ ( __UpperCAmelCase ) -> Tuple:
'''simple docstring'''
def wrapper(*__UpperCAmel... | 13 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a = logging.get_logger(__name__)
a = {
"asapp/sew-tiny-100k": "https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json",
# See a... | 13 | 1 |
'''simple docstring'''
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
a = "\\n@misc{wu2016googles,\n title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},\n author=... | 13 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase = 1 , __UpperCAmelCase = 1000 ) -> int:
'''simple docstring'''
__SCREAMING_SNAKE_CASE = 1
__SCREAMING_SNAKE_CASE = 0
for divide_by_number in range(__UpperCAm... | 13 | 1 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase = 100 ) -> int:
'''simple docstring'''
__SCREAMING_SNAKE_CASE = n * (n + 1) * (2 * n + 1) / 6
__SCREAMING_SNAKE_CASE = (n * (n + 1) / 2) ** 2
return int(square_of_s... | 13 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin
if is_torch_available():
import... | 13 | 1 |
'''simple docstring'''
from statistics import mean
import numpy as np
def __magic_name__ ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ) -> list:
'''simple docstring'''
__SCREAMING_SNAKE_CASE = 0
... | 13 |
'''simple docstring'''
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def __magic_name__ ( __UpperCAmelCase ) -> Tuple:
'''simple docstring'''
def wrapper(*__UpperCAmel... | 13 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
a = {
"configuration_vision_encoder_decoder": ["VisionEncoderDecoderConfig", "VisionEncoderD... | 13 |
'''simple docstring'''
import time
from contextlib import contextmanager
from pathlib import Path
import pytest
import requests
from huggingface_hub.hf_api import HfApi, HfFolder
a = "__DUMMY_TRANSFORMERS_USER__"
a = "Dummy User"
a = "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"
... | 13 | 1 |
'''simple docstring'''
from __future__ import annotations
import os
from collections.abc import Mapping
a = tuple[int, int]
class __a :
def __init__( self : List[Any] ,lowerCamelCase : set[int] ,lowerCamelCase : Mapping[EdgeT, int] ):
'''simple docs... | 13 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
a = logging.get_logger(__name__)
a = {
"ut/deta": "https://huggingface.co/ut/deta/resolve/main/config.json",
}
class __a ( ... | 13 | 1 |
import multiprocessing
import os
from typing import BinaryIO, Optional, Union
import fsspec
from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, Path... | 700 |
'''simple docstring'''
import flax.linen as nn
import jax
import jax.numpy as jnp
class __a ( nn.Module ):
__UpperCamelCase : int
__UpperCamelCase : jnp.dtype = jnp.floataa
def UpperCAmelCase__ ( self : List[Any] ):
'''simple docstring'''
__SC... | 13 | 0 |
'''simple docstring'''
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image
from transforme... | 701 |
'''simple docstring'''
import sys
from collections import defaultdict
class __a :
def __init__( self : Dict ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = []
def UpperCAmelCase__ ( self : List[Any] ,lowerCamelCase : ... | 13 | 0 |
'''simple docstring'''
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBackend,
default_hp... | 702 |
'''simple docstring'''
import os
import string
import sys
a = 1 << 8
a = {
"tab": ord("\t"),
"newline": ord("\r"),
"esc": 27,
"up": 65 + ARROW_KEY_FLAG,
"down": 66 + ARROW_KEY_FLAG,
"right": 67 + ARROW_KEY_FLAG,
"left": 68 + ARROW_KEY_FLAG,
"mod_int": 91... | 13 | 0 |
'''simple docstring'''
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import TimesformerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow... | 703 |
'''simple docstring'''
from __future__ import annotations
import bisect
def __magic_name__ ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = 0 , __UpperCAmelCase = -1 ) -> int:
'''simple docstring'''
if hi < 0:
__SCREAMING_SNAKE_CAS... | 13 | 0 |
'''simple docstring'''
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def __magic_name__ ( __UpperCAmelCase , __Up... | 704 |
'''simple docstring'''
import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
a = logging.get_logger(__name__)
class __a ( _snake_case ):
__UpperCamelCase : int... | 13 | 0 |
'''simple docstring'''
from functools import reduce
a = (
"73167176531330624919225119674426574742355349194934"
"96983520312774506326239578318016984801869478851843"
"85861560789112949495459501737958331952853208805511"
"12540698747158523863050715693290963295227443043557"
"668966489... | 705 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a = {"configuration_sew": ["SEW_PRETRAINED_CONFIG_ARCHIVE_MAP", "SEWConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvail... | 13 | 0 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase , __UpperCAmelCase ) -> list[str]:
'''simple docstring'''
return [sentence[i : i + ngram_size] for i in range(len(lowerCamelCase_ ) - ngram_size + 1 )]
if __name__ == "__main__":
from... | 706 |
'''simple docstring'''
import requests
from bsa import BeautifulSoup
def __magic_name__ ( __UpperCAmelCase = "AAPL" ) -> str:
'''simple docstring'''
__SCREAMING_SNAKE_CASE = f"""https://in.finance.yahoo.com/quote/{symbol}?s={symbol}"""
__SCREAMI... | 13 | 0 |
'''simple docstring'''
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple
import yaml
class __a ( yaml.SafeLoader ):
def UpperCAmelCase__ ( self : Optional[int] ,lowerCamelCase : List[str] ):
'''simple docstring'''
... | 707 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase ) -> bool:
'''simple docstring'''
if num < 0:
return False
__SCREAMING_SNAKE_CASE = num
__SCREAMING_SNAKE_CASE = 0
while num > 0:
__SCREAMING_SNAKE_... | 13 | 0 |
'''simple docstring'''
from __future__ import annotations
import queue
class __a :
def __init__( self : Optional[int] ,lowerCamelCase : List[str] ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = data
__SCREAMING_SNAKE_CASE = ... | 708 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
a = list[list[float | int]]
def __magic_name__ ( __UpperCAmelCase , __UpperCAmelCase ) -> Matrix:
'''simple docstring'''
__SCREAMING_SNAKE_CASE ... | 13 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
a = {'configuration_dpt': ['DPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DPTConfig']}
try:
... | 709 |
'''simple docstring'''
from collections import UserDict
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available... | 13 | 0 |
'''simple docstring'''
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require... | 710 |
'''simple docstring'''
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be check... | 13 | 0 |
'''simple docstring'''
import importlib.metadata
import operator
import re
import sys
from typing import Optional
from packaging import version
a = {
'<': operator.lt,
'<=': operator.le,
'==': operator.eq,
'!=': operator.ne,
'>=': operator.ge,
'>': operator.gt,
}
def __ma... | 711 |
'''simple docstring'''
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
BartForSequen... | 13 | 0 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase , __UpperCAmelCase ) -> bool:
'''simple docstring'''
__SCREAMING_SNAKE_CASE = len(__lowerCAmelCase ) + 1
__SCREAMING_SNAKE_CASE = len(__lowerCAmelCase ) + ... | 712 |
'''simple docstring'''
from ...utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_torch_available,
is_transformers_available,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAv... | 13 | 0 |
'''simple docstring'''
import argparse
import json
import logging
import os
import sys
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, get_gpu_count, slow
a = [
os.path.join(os.path.dirname(__file__), dirname)
for dirname in [
"text-classification",
... | 713 |
'''simple docstring'''
import requests
from bsa import BeautifulSoup
def __magic_name__ ( __UpperCAmelCase , __UpperCAmelCase ) -> str:
'''simple docstring'''
__SCREAMING_SNAKE_CASE = BeautifulSoup(requests.get(__UpperCAmelCase , params... | 13 | 0 |
from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors
def __magic_name__ ( __UpperCAmelCase ) -> Union[str, Any]:
'''simple docstring'''
__SCREAMING_SNAKE_CASE = prime_factors(__A )
if is_square_free(_... | 714 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a = logging.get_logger(__name__)
a = {
"camembert-base": "https://huggingface.co/ca... | 13 | 0 |
import unittest
from transformers import (
MODEL_FOR_OBJECT_DETECTION_MAPPING,
AutoFeatureExtractor,
AutoModelForObjectDetection,
ObjectDetectionPipeline,
is_vision_available,
pipeline,
)
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_pytes... | 715 |
'''simple docstring'''
import inspect
import unittest
import numpy as np
from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_t... | 13 | 0 |
'''simple docstring'''
from __future__ import annotations
from random import choice
def __magic_name__ ( __UpperCAmelCase ) -> Optional[int]:
'''simple docstring'''
return choice(_lowerCamelCase )
def __magic_name__ ( __UpperCAmelCase ... | 716 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a = logging.get_logger(__name__)
a = {
"asapp/sew-tiny-100k": "https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json",
# See a... | 13 | 0 |
'''simple docstring'''
from __future__ import annotations
from bisect import bisect_left
from functools import total_ordering
from heapq import merge
@total_ordering
class __a ( _UpperCAmelCase ):
def __lt__( self : Optional[int] ,lowerCamelCase : List[str] ):
'''si... | 717 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase = 1 , __UpperCAmelCase = 1000 ) -> int:
'''simple docstring'''
__SCREAMING_SNAKE_CASE = 1
__SCREAMING_SNAKE_CASE = 0
for divide_by_number in range(__UpperCAm... | 13 | 0 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.util... | 718 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin
if is_torch_available():
import... | 13 | 0 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import torch
import torchaudio
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from transformers import ASTConfig, ASTFeatureExtractor, ASTForAudioClassification
from transformers.utils import logging
lo... | 719 |
'''simple docstring'''
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def __magic_name__ ( __UpperCAmelCase ) -> Tuple:
'''simple docstring'''
def wrapper(*__UpperCAmel... | 13 | 0 |
'''simple docstring'''
from __future__ import annotations
def __magic_name__ ( __UpperCAmelCase , __UpperCAmelCase = None , __UpperCAmelCase = None ) -> List[Any]:
'''simple docstring'''
if start is None:
__SCREAMING_SNAKE_CASE = ... | 720 |
'''simple docstring'''
import time
from contextlib import contextmanager
from pathlib import Path
import pytest
import requests
from huggingface_hub.hf_api import HfApi, HfFolder
a = "__DUMMY_TRANSFORMERS_USER__"
a = "Dummy User"
a = "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"
... | 13 | 0 |
'''simple docstring'''
from json import JSONDecodeError # Workaround for requests.exceptions.JSONDecodeError
import requests
def __magic_name__ ( __UpperCAmelCase = "isbn/0140328726" ) -> Any:
'''simple docstring'''
__SCREAMING_SNAKE_CASE = olid.... | 721 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
a = logging.get_logger(__name__)
a = {
"ut/deta": "https://huggingface.co/ut/deta/resolve/main/config.json",
}
class __a ( ... | 13 | 0 |
import heapq as hq
import math
from collections.abc import Iterator
class __a :
def __init__( self : Tuple ,lowerCamelCase : int ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = str(id_ )
__SCREAMING_SNAKE_CASE = None
... | 700 |
'''simple docstring'''
import flax.linen as nn
import jax
import jax.numpy as jnp
class __a ( nn.Module ):
__UpperCamelCase : int
__UpperCamelCase : jnp.dtype = jnp.floataa
def UpperCAmelCase__ ( self : List[Any] ):
'''simple docstring'''
__SC... | 13 | 0 |
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.activations import gelu_new, gelu_python, get_activation
@require_torch
class __a( unittest.TestCase... | 701 |
'''simple docstring'''
import sys
from collections import defaultdict
class __a :
def __init__( self : Dict ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = []
def UpperCAmelCase__ ( self : List[Any] ,lowerCamelCase : ... | 13 | 0 |
'''simple docstring'''
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Value
from .base import TaskTemplate
@dataclass(frozen=UpperCamelCase_ )
class __a ( UpperCamelCase_ ):
__UpperCamelCase : List[Any] ... | 702 |
'''simple docstring'''
import os
import string
import sys
a = 1 << 8
a = {
"tab": ord("\t"),
"newline": ord("\r"),
"esc": 27,
"up": 65 + ARROW_KEY_FLAG,
"down": 66 + ARROW_KEY_FLAG,
"right": 67 + ARROW_KEY_FLAG,
"left": 68 + ARROW_KEY_FLAG,
"mod_int": 91... | 13 | 0 |
'''simple docstring'''
from random import randint
from tempfile import TemporaryFile
import numpy as np
def __magic_name__ ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ) -> Optional[int]:
'''simple docstring'''
__SCREA... | 703 |
'''simple docstring'''
from __future__ import annotations
import bisect
def __magic_name__ ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = 0 , __UpperCAmelCase = -1 ) -> int:
'''simple docstring'''
if hi < 0:
__SCREAMING_SNAKE_CAS... | 13 | 0 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .to... | 704 |
'''simple docstring'''
import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
a = logging.get_logger(__name__)
class __a ( _snake_case ):
__UpperCamelCase : int... | 13 | 0 |
'''simple docstring'''
import inspect
import unittest
class __a ( unittest.TestCase ):
'''simple docstring'''
def UpperCAmelCase__ ( self : List[str] ):
'''simple docstring'''
try:
import diffusers # noqa: F401
except ImportError:
... | 705 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a = {"configuration_sew": ["SEW_PRETRAINED_CONFIG_ARCHIVE_MAP", "SEWConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvail... | 13 | 0 |
'''simple docstring'''
a = "Alexander Joslin"
import operator as op
from .stack import Stack
def __magic_name__ ( __UpperCAmelCase ) -> int:
'''simple docstring'''
__SCREAMING_SNAKE_CASE = {"""*""": op.mul, """/""": op.truediv, """+"... | 706 |
'''simple docstring'''
import requests
from bsa import BeautifulSoup
def __magic_name__ ( __UpperCAmelCase = "AAPL" ) -> str:
'''simple docstring'''
__SCREAMING_SNAKE_CASE = f"""https://in.finance.yahoo.com/quote/{symbol}?s={symbol}"""
__SCREAMI... | 13 | 0 |
'''simple docstring'''
import argparse
import torch
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt
if __name__ == "__main__":
a = argparse.ArgumentParser()
parser.add_argument(
"--checkpoint_path", default=None, typ... | 707 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase ) -> bool:
'''simple docstring'''
if num < 0:
return False
__SCREAMING_SNAKE_CASE = num
__SCREAMING_SNAKE_CASE = 0
while num > 0:
__SCREAMING_SNAKE_... | 13 | 0 |
'''simple docstring'''
import baseaa
def __magic_name__ ( __UpperCAmelCase ) -> bytes:
'''simple docstring'''
return baseaa.aaaencode(string.encode("""utf-8""" ) )
def __magic_name__ ( __UpperCAmelCase ) -> str:
'''simp... | 708 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
a = list[list[float | int]]
def __magic_name__ ( __UpperCAmelCase , __UpperCAmelCase ) -> Matrix:
'''simple docstring'''
__SCREAMING_SNAKE_CASE ... | 13 | 0 |
'''simple docstring'''
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
a = logging.get_logger(__name__)
a ... | 709 |
'''simple docstring'''
from collections import UserDict
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available... | 13 | 0 |
'''simple docstring'''
from math import isqrt
def __magic_name__ ( __UpperCAmelCase ) -> List[str]:
'''simple docstring'''
return all(number % divisor != 0 for divisor in range(2 , isqrt(_snake_case ) + 1 ) )
def __m... | 710 |
'''simple docstring'''
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be check... | 13 | 0 |
'''simple docstring'''
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transfo... | 711 |
'''simple docstring'''
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
BartForSequen... | 13 | 0 |
'''simple docstring'''
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import DeiTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test... | 712 |
'''simple docstring'''
from ...utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_torch_available,
is_transformers_available,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAv... | 13 | 0 |
'''simple docstring'''
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class __a ( UpperCAmelCase__, UpperCAmelCase__ ):
@register_to_config
def __init__( self : Dict ,*,
lowerCamelCase ... | 713 |
'''simple docstring'''
import requests
from bsa import BeautifulSoup
def __magic_name__ ( __UpperCAmelCase , __UpperCAmelCase ) -> str:
'''simple docstring'''
__SCREAMING_SNAKE_CASE = BeautifulSoup(requests.get(__UpperCAmelCase , params... | 13 | 0 |
from collections import defaultdict
from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst
def __magic_name__ ( ) -> List[str]:
'''simple docstring'''
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = 9, 14 # noqa: F841
__S... | 714 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a = logging.get_logger(__name__)
a = {
"camembert-base": "https://huggingface.co/ca... | 13 | 0 |
from __future__ import annotations
import requests
a = set(
"approved_at_utc approved_by author_flair_background_color\nauthor_flair_css_class author_flair_richtext author_flair_template_id author_fullname\nauthor_premium can_mod_post category clicked content_categories created_utc downs\nedited... | 715 |
'''simple docstring'''
import inspect
import unittest
import numpy as np
from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_t... | 13 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
import torch
from ..models.auto import AutoModelForVisualQuestionAnswering, AutoProcessor
from ..utils import requires_backends
from .base import PipelineTool
if TYPE_CHECKING:
from PIL import Image
class __a ( UpperCamelCase__ ):
__Up... | 716 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a = logging.get_logger(__name__)
a = {
"asapp/sew-tiny-100k": "https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json",
# See a... | 13 | 0 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a = logging.get_logger(__name__)
a = {
"asapp/sew-d-tiny-100k": "https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json",
# S... | 717 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase = 1 , __UpperCAmelCase = 1000 ) -> int:
'''simple docstring'''
__SCREAMING_SNAKE_CASE = 1
__SCREAMING_SNAKE_CASE = 0
for divide_by_number in range(__UpperCAm... | 13 | 0 |
'''simple docstring'''
import json
import os
import tempfile
import datasets
from utils import generate_example_dataset, get_duration
a = 50000
a = 5000
a , a = os.path.split(__file__)
a = os.path.join(RESULTS_BASEPATH, "results", RESULTS_FILENAME.replace(".... | 718 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin
if is_torch_available():
import... | 13 | 0 |
'''simple docstring'''
import os
import string
import sys
a = 1 << 8
a = {
'tab': ord("\t"),
'newline': ord("\r"),
'esc': 27,
'up': 65 + ARROW_KEY_FLAG,
'down': 66 + ARROW_KEY_FLAG,
'right': 67 + ARROW_KEY_FLAG,
'left': 68 + ARROW_KEY_FLAG,
'mod_int': 91... | 719 |
'''simple docstring'''
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def __magic_name__ ( __UpperCAmelCase ) -> Tuple:
'''simple docstring'''
def wrapper(*__UpperCAmel... | 13 | 0 |
'''simple docstring'''
import collections
import os
import re
from pathlib import Path
a = "src/transformers"
# Matches is_xxx_available()
a = re.compile(r"is\_([a-z_]*)_available()")
# Catches a one-line _import_struct = {xxx}
a = re.compile(r"^_import_structure\s+=\s+\{(... | 720 |
'''simple docstring'''
import time
from contextlib import contextmanager
from pathlib import Path
import pytest
import requests
from huggingface_hub.hf_api import HfApi, HfFolder
a = "__DUMMY_TRANSFORMERS_USER__"
a = "Dummy User"
a = "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"
... | 13 | 0 |
'''simple docstring'''
import hashlib
import unittest
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
... | 721 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
a = logging.get_logger(__name__)
a = {
"ut/deta": "https://huggingface.co/ut/deta/resolve/main/config.json",
}
class __a ( ... | 13 | 0 |
from dataclasses import dataclass, field
from typing import Optional
@dataclass
class __a :
__UpperCamelCase : Optional[str] = field(
default='codeparrot/codeparrot', metadata={'help': 'Model name or path of model to be trained.'} )
__UpperCamelCase : Optional[str] = field(... | 700 |
'''simple docstring'''
import flax.linen as nn
import jax
import jax.numpy as jnp
class __a ( nn.Module ):
__UpperCamelCase : int
__UpperCamelCase : jnp.dtype = jnp.floataa
def UpperCAmelCase__ ( self : List[Any] ):
'''simple docstring'''
__SC... | 13 | 0 |
'''simple docstring'''
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ASTConfig
from transformers.testing_utils import require_torch, require_torchaudio, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_torchaudio_ava... | 701 |
'''simple docstring'''
import sys
from collections import defaultdict
class __a :
def __init__( self : Dict ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = []
def UpperCAmelCase__ ( self : List[Any] ,lowerCamelCase : ... | 13 | 0 |
'''simple docstring'''
import torch
def __magic_name__ ( ) -> Tuple:
'''simple docstring'''
if torch.cuda.is_available():
__SCREAMING_SNAKE_CASE = torch.cuda.device_count()
else:
__SCREAMING_SNAKE_CASE = 0
print(f"""S... | 702 |
'''simple docstring'''
import os
import string
import sys
a = 1 << 8
a = {
"tab": ord("\t"),
"newline": ord("\r"),
"esc": 27,
"up": 65 + ARROW_KEY_FLAG,
"down": 66 + ARROW_KEY_FLAG,
"right": 67 + ARROW_KEY_FLAG,
"left": 68 + ARROW_KEY_FLAG,
"mod_int": 91... | 13 | 0 |
'''simple docstring'''
from PIL import Image
def __magic_name__ ( __UpperCAmelCase , __UpperCAmelCase ) -> List[Any]:
'''simple docstring'''
__SCREAMING_SNAKE_CASE = (259 * (level + 255)) / (255 * (259 - level))
def ... | 703 |
'''simple docstring'''
from __future__ import annotations
import bisect
def __magic_name__ ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = 0 , __UpperCAmelCase = -1 ) -> int:
'''simple docstring'''
if hi < 0:
__SCREAMING_SNAKE_CAS... | 13 | 0 |
'''simple docstring'''
import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=logging.INFO)
a ... | 704 |
'''simple docstring'''
import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
a = logging.get_logger(__name__)
class __a ( _snake_case ):
__UpperCamelCase : int... | 13 | 0 |
'''simple docstring'''
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
a = logging.get_logger(__name__)
a = OrderedDict(
[
... | 705 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a = {"configuration_sew": ["SEW_PRETRAINED_CONFIG_ARCHIVE_MAP", "SEWConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvail... | 13 | 0 |
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
if is_torch_available():
import torch
from transformers import XLMRobertaModel
@require_sentencepiece
@requi... | 706 |
'''simple docstring'''
import requests
from bsa import BeautifulSoup
def __magic_name__ ( __UpperCAmelCase = "AAPL" ) -> str:
'''simple docstring'''
__SCREAMING_SNAKE_CASE = f"""https://in.finance.yahoo.com/quote/{symbol}?s={symbol}"""
__SCREAMI... | 13 | 0 |
'''simple docstring'''
from __future__ import annotations
a = {
"A": ["B", "C", "E"],
"B": ["A", "D", "E"],
"C": ["A", "F", "G"],
"D": ["B"],
"E": ["A", "B", "D"],
"F": ["C"],
"G": ["C"],
}
class __a :
def __init__( self : List[Any] ,lowerCamelC... | 707 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase ) -> bool:
'''simple docstring'''
if num < 0:
return False
__SCREAMING_SNAKE_CASE = num
__SCREAMING_SNAKE_CASE = 0
while num > 0:
__SCREAMING_SNAKE_... | 13 | 0 |
'''simple docstring'''
import argparse
from collections import defaultdict
def __magic_name__ ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ) -> Optional[int]:
'''simple docstring'''
__SCREAMIN... | 708 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
a = list[list[float | int]]
def __magic_name__ ( __UpperCAmelCase , __UpperCAmelCase ) -> Matrix:
'''simple docstring'''
__SCREAMING_SNAKE_CASE ... | 13 | 0 |
'''simple docstring'''
import tempfile
import unittest
import numpy as np
from diffusers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionPipeline,
PNDMScheduler,
)
from diffusers.ut... | 709 |
'''simple docstring'''
from collections import UserDict
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available... | 13 | 0 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import MobileBertConfig, is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
fr... | 710 |
'''simple docstring'''
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be check... | 13 | 0 |
'''simple docstring'''
from typing import Any
class __a :
def __init__( self : Union[str, Any] ,lowerCamelCase : List[Any] ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = data
__SCREAMING_SNAKE_CASE = None
class __a :... | 711 |
'''simple docstring'''
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
BartForSequen... | 13 | 0 |
'''simple docstring'''
import argparse
import json
from typing import List
from ltp import LTP
from transformers import BertTokenizer
def __magic_name__ ( __UpperCAmelCase ) -> Optional[int]:
'''simple docstring'''
if (
(cp >= 0X4E00 and cp <= 0X9FFF)
or... | 712 |
'''simple docstring'''
from ...utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_torch_available,
is_transformers_available,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAv... | 13 | 0 |
'''simple docstring'''
import unittest
from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY... | 713 |
'''simple docstring'''
import requests
from bsa import BeautifulSoup
def __magic_name__ ( __UpperCAmelCase , __UpperCAmelCase ) -> str:
'''simple docstring'''
__SCREAMING_SNAKE_CASE = BeautifulSoup(requests.get(__UpperCAmelCase , params... | 13 | 0 |
def __magic_name__ ( __UpperCAmelCase = 100 ) -> Any:
'''simple docstring'''
__SCREAMING_SNAKE_CASE = (n * (n + 1) // 2) ** 2
__SCREAMING_SNAKE_CASE = n * (n + 1) * (2 * n + 1) // 6
return sum_cubes - sum_squares
if __name__... | 714 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a = logging.get_logger(__name__)
a = {
"camembert-base": "https://huggingface.co/ca... | 13 | 0 |
import argparse
import torch
from transformers import YosoConfig, YosoForMaskedLM
def __magic_name__ ( __UpperCAmelCase ) -> List[str]:
'''simple docstring'''
if "model" in orig_key:
__SCREAMING_SNAKE_CASE = orig_key.replace("""model.""" ... | 715 |
'''simple docstring'''
import inspect
import unittest
import numpy as np
from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_t... | 13 | 0 |
'''simple docstring'''
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class __a :
def __init__( self : Optional[Any] ,lowerCamelCase : List[str]=2 ,lowerCamelCase : Tuple=3 ,lowerCamelCas... | 716 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a = logging.get_logger(__name__)
a = {
"asapp/sew-tiny-100k": "https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json",
# See a... | 13 | 0 |
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